v/c ratio based on road geometrical elements using IoT sensors based on artificial neural network modeling

Abstract

The study attempts to analyze the variation of v/c ratio with respect to the road way geometric parameters. The study uses two different methods such as Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) to develop models in order to predict the v/c ratiousing IoT Sensors. The analysis of the field data is used to compute the v/c ratios for different mid-blocks and signalized intersections. Two different methods are used to develop a model to predict the v/c ratio with respect to the change in independent variables such as number of lanes, number of footpaths, width of the road, length of the road, number of types of vehicles moving on that particular roads or intersections, average speed of the vehicles. Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) methods have been used to develop the model in the present study.

Keywords

• IoT sensors
• Neural networks
• Linear regression
• Machine learning
• Geometrical elements